PBDD: A Prompt-Based Learning Approach for Few-Shot Social Media Depression Detection

Automated detection of depressive moods from social media holds great promise for early mental health intervention, yet existing multimodal approaches typically require large quantities of annotated data and extensive feature engineering, impeding their deployment in real‐world settings where labels are scarce. To address this challenge, we propose prompt‐based depression detection (PBDD), a novel prompt‐based few‐shot learning framework that leverages frozen pretrained language and vision models to identify depression indicators from paired text‐image posts without fine‐tuning. Our method begins with rigorous data cleaning and sampling to construct a high‐quality few‐shot dataset, then encodes text via a masked language model and images via a self‐supervised rotation‐prediction task to capture deep semantic cues. Multimodal representations are seamlessly fused into a unified prompt template containing a [MASK] token, enabling the pre‐trained model to infer depressive states by language completion. Extensive experiments on both large‐scale and 1 % few‐shot subsets demonstrate that PBDD consistently outperforms state‐of‐the‐art baselines, achieving significant gains in accuracy and Macro‐F1. These results validate the effectiveness and scalability of our framework for depression detection under severe label scarcity, offering a practical solution for real‐time mental health monitoring in social media environments.